Ontological Navigation

Ontological navigation will continue as long as the presentation of relevant information can be refined to ‘Intuitive Search’ and ‘Data Driven Research’ for a business world that is Gasping in a Gulf of information.

Transcript of "Ontological Navigation"

1.
Ontological Navigation
The landscape of ontology is an organic process in a collaborative sense and built around three broad processes
(Input, Organization, and Output) with two actors in each (Human and Artificial). The content of these categories is
constantly being updated in the open-source community and the business world is adopting these innovations as
quickly as their clients can acclimate to this new found source of relevance. The process will continue as long as the
presentation of relevant information can be refined to ‘Intuitive Search’ and ‘Data Driven Research’ for a business
world that is Gasping in a Gulf of information.
INPUT ORGANIZATION OUTPUT
-HI (HUMAN INTELLIGENCE) -HI -HI
-COMMUNITY GENERATED ONTOLOGY -Protégé ONTOLOGY EDITOR -Protégé VISUALIZATIONS
-4D ONTOLOGY -COLLABORATIVE WHITE PAGE -WHITE PAGE PDF
(INCLUDES TIME FROM EMERGING TO MAINSTREAM)
-COMMUNITY SUGGESTED ONTOLOGY -NEEMEE COLLECTIONS -TAG CLOUD
-AI (ARTIFICIAL INTELLIGENCE) -AI -AI
-SEMANTIC SIGNATURES -SEMANTIC HACKER -WEIGHTED TAGS
-QUINTURA -QUINTURA -KEYWORD CLOUD
-ZEMANTA -CLUSTERING ALGORITHMS -SEMANTICALLY SIMILAR TAGS

2.
HI- ONTOLOGY BEGINS WITH YOU
MS
-COMMUNITY GENERATED ONTOLOGY
Community driven Ontology begins at the level of
a thought capture. A community speciﬁc
Ontology is organically cultivated by allowing
users to direct thoughts, and their associated
tags, to speciﬁc users. The icons shown can
represent individuals, user groups, collections (as
currently exists within Neemee), or Waves
-4D ONTOLOGY
CultureWaves tracks cultural evidence over time.
By allowing users to identify evidence relative to
G perceived cultural norms, a personal ontology COMMUNITY GENERATED
based in time can emerge. This can be used to
identify users that may be more or less
mainstream across the whole of the Neemee ONTOLOGY BEGINS WITH
community. By identifying the pervasiveness of a
thought at the point of capture, a time based USERS MAKING CHOICES
model of Neemee evidence can evolve across
multiple CW quarterly reports
THROUGH A COMMON
-COMMUNITY SUGGESTED ONTOLOGY VOCABULARY AND
Using ‘auto suggest’ technology as you type,
alphabetical tag cloud design, and user-selected COLLABORATING
tags, users can choose from multiple existing
tags in Neemee. This reinforces Neemee’s
existing ontological organization and fosters a
THROUGH COMMON
E
sense of collaboration eliminating the burden of
‘tagging in the dark’ IDEAS

3.
AI- ONTOLOGY IS ABOUT CHOICES FROM RELEVANT SOURCES
Zemanta offers Neemee the opportunity to dynamically source the entire site for Ontological content and bring that content
back to the level of a single thought. Users can add links and tags that they ﬁnd relevant from the Neemee ontology or add
their own. The Neemee ontology is dynamically sourced and kept up to date through community participation.
-WEIGHTED TAG CLOUD
A weighted tag cloud shows the prevalence of more frequent
tags to thoughts and diminishes tags with fewer connections.
Ontology is not a static view and needs constant reinforcement,
keeping ontology graphics consistent across page views and user
interfaces allows users to take a dashboard view of ontological
relevance at every layer of Neemee
-COMMUNITY LINKED THOUGHTS
Community linked thoughts create semantic connections at the
granular level of a single thought. Currently, a thought page
has no other links outside of itself other than the tag list. By
including thought links at the level of a thought, the ontologic
web of Neemee is reinforced. These are dynamically updated
every time a thought is viewed, thus updating the semantic
integration of a thought regardless of the date of capture.
-COMMUNITY UPDATED TAGS
Community updated tags are dynamically sourced from within
and outside of Neemee. These can reference thoughts
ontologically related to the thought being viewed regardless of
the date of capture. Users can choose which tags to add to the
thought they are viewing or add their own. This continual
update of the ontologic information in Neemee allows relevance
to be built through meaning as well as the date of capture.

4.
AI- ONTOLOGY IS ABOUT THE CROWD TEACHING MACHINES
Semantic Signatures provides quantiﬁable data through an AI ‘learning’ process that reﬁnes relevance through community
participation. By editing articles from the web through the capture process, relevance is deﬁned. AI can help reﬁne and
deﬁne relevance.
Semantic Signatures uses an AI semantic training process with an existing
ontological data base from Neemee. The algorithm can create tag strings, or
‘scaled language’, with the top line ontological category names taken from
the tag data base and then drills down to the common descriptor tags.
The trainable Semantic technology automatically generates a semantic data
base during the training phase. This data base then contains the ontology
known to be relevant to Neemee thoughts and automatically provides a
“deﬁnition” of each term using tag strings.
Tag Strings can be used to deﬁne high level concepts relative to thoughts
and provide conceptual relevance for Waves, White papers, Consulting, etc.
The resulting tag strings can then be tracked over time to provide a
conceptual history of Waves and Human Truths. How do the consumer
concepts related to “I want what I want, when I want it.” change over time
and what kind of trends can our consulting group deﬁne as a history and a
future.
Manually, through conjecture, CW can interpret Neemee thoughts for our
clients but collaborative relevance remains elusive. Is the thought to the
left about health or ﬁnance? Both for sure, but what are the relativistic
weights of those two concepts? Through an AI application of ontology
interpretation, CW consulting can build veriﬁable data over time that brings
credence to the Collaborative Ontology building in which Neemee is based.
As the AI learns, we learn. As as we learn, the AI learns. AI provides a
consistent veriﬁable record of our accuracy and helps us have conﬁdence in
our collaborative Ontology

5.
AI- ONTOLOGY IS ABOUT VISUALIZING MEANING
Quintura provides a dynamically sourced search cloud and search results that changes as Neemee users select one ontological
term or another. This dynamic visualization encourages community participation with the ontology and reﬁnes relevance
through selection.
Quintura creates interactive site search and ontology navigation. The Search
cloud engages users in site search more intuitively than regular tag clouds
resulting in greater ontological relevance and deeper understanding of
related search terms. The interactive search box provides dynamically
changing results on A mouse over of terms resulting in fewer clicks and
page views and the ability to intuitively search through ontologically related
concepts
Neemee users need to feel participation in their navigation through neemee.
The experience is equal to the insights gleaned. Quintura offers a
dynamically changing search cloud and results. The organization and search
results offered by Quintura is based in ontology and relevance. The API is
customizable and can be geared to return high level CW search results with
the associated crowd sourced tags.
If ontology is seen by Neemee users as a system of organization that is
static, that they must ascribe to, then the relevance of the ontology will
consistently be viewed as something outside their normal way of thinking.
Ontology must be seen as a way of clarifying peoples normal method of
thinking about things, a way of organizing their thoughts through a shared
vocabulary. The visualization of ontology is as important as the architecture
of the ontology itself. Dynamic visualization encourages community
participation and it is through this participation that the ontology will
organically become more relevant.

6.
HI- ONTOLOGY IS ABOUT INTENTIONALLY PLACED MEANING
Each Neemee user has a unique perspective that is unequally distributed and is weighted toward some things rather than
others. This is Neemee’s strength and the power of mass collaboration. Given the ability to express this diversity and coupled
with the ontological data that accompanies each Neemee thought, Neemee’s collaborative ontology is as intuitive as daily life
and as informative as a conversation with a thousand experts.
Polyvore combines collaborative data base storage with white page
composition. Ontology is about relational meaning where the proximity of
things deﬁnes the strength of connection. Allowing Neemee users to deﬁne,
intuitively, the relative strength and weakness of the relations between
Neemee evidence is at the heart of a collaborative ontology and
Culturewaves insights. Neemee users ability to reﬁne the evidence within a
collection coupled with semantic data creates crafted and deﬁned relevance.
Ontology is a result meaningful relationships rather than a prescribed rule
set to follow. It begins at the granular level with users making informed
choices about what Neemee captures mean to them through a collaborative
vocabulary. Ontology is not about singular deﬁnitions, it is the sum of the
crowd that collectively deﬁnes meaning. Each thought in Neemee carries
with it its own semantic signature and when intuitively and intentionally
placed in relation to one another, these signatures add up to deﬁne high
level ontologic concepts and insights.
How is a ‘Human Truth’ deﬁned when it’s translated into tangible evidence
through Neemee evidence and how does this deﬁnition change over time.
User created collaborative white pages bring the whole of the Neemee
community to bear on these questions. Their answers then deﬁne the
structure of the Neemee ontology and its relational architecture over time.
The white page is where the ‘Human Truth’ meets the street, it is the Wave
pages, and its where ontology takes on a human face.

7.
HI- ONTOLOGY IS ABOUT HUMANS STRUCTURING KNOWLEDGE
Ontology editors provide an environment where a community of users devoted to making meaning can cultivate a crowd sourced
body of evidence to create collaborative connections. The Neemee ontology editing suite is the space of innovation and insight
where seemingly unrelated concepts become codiﬁed through ‘Red Thread’ relationships. An intuitive interface combined with
data driven structure creates a space where scientists work with artists, where analysts work with the ‘youniverse’.
Protégé ontology editor is a collaborative tool for managing ontological concepts and collaborative data bases. Neemee currently exists as a static
structure built by individuals. Incorporating a collaborative tool for managing that structure creates an environment where users have a level of
participation in the ontology that goes beyond merely being observers. An environment where the dynamic and organically grown Neemee
ontological structure can be edited by the mass of Neemee users will attract members of the Knowledge Management community to cultivate
ontological meaning. Protégé allows users edit structures of meaning. Given a high level editor, those users with the intention and desire to
participate in an intuitive and science based insights tool will dive in and begin to use a crowd sourced body of knowledge to develop a semantic
web of relevance and meaning. This level of contributors will drive consulting dollars where clients’ raw data can be modeled through to traceable
evidence based insights.

8.
HI- Protégé AND THE NEEMEE ONTOLOGY
Protégé is an ontology editor used for
editing and modeling concepts and their
semantic relationships. The Protégé editing
suite allows for web based semantic data to
be imported from Neemee’s relational
database deﬁned by speciﬁc relationships.
In short, Neemee’s tag database can be
imported into the Protégé ontology editor,
the relationships between the tags can be
deﬁned, and those relationships can be
visualized for client speciﬁc needs or
CultureWaves data modeling.

11.
Protégé OPPORTUNITIES AND APPLICATIONS
THROUGH AN ONTOLOGY EDITOR NEEMEE MAINTAINED AND MODELED FOR MANY DIFFERENT APPLICATIONS
Language and Spelling:
Identify conceptually similar terms and merge them to avoid reducing relevance through multiple tags with the same meaning.
Apply language dictionaries to translate Neemee.com into multiple languages and collaboratively reﬁne deﬁnitions across languages.
Implement dictionaries and thesauruses to automatically correct misspelled tags to avoid multiple instances of commonly misspelled
words and unique words that are difﬁcult to spell correctly.
Collaboration and Software Platforms:
Through the Protégé collaborative ontology Wiki, word deﬁnitions can be edited by groups of users to reﬁne meaning and generate
a common vocabulary.
A structured ontology can be exported to multiple software platforms such as Excell, XML, RDF, OWL, Analytics software, and
various other report generating plugins in the ProtegePluginsLibrary
Clients can create their own ontology that is interpretable by and relevant to the Neemee ontology, clients can then create
applications that address speciﬁc search functions and data exports for client speciﬁc ontologies.
Visualization and Reporting:
A structured ontology can be exported in real time to the many visualization plugins developed by the Protégé community. A
visually modeled ontology deﬁnes organically changing relationships and relevancies within the Neemee ontology as they change.
A structured ontology can be exported to multiple software platforms such as Excell, XML, RDF, OWL, Analytics software, and
various other report generating plugins in the ProtegePluginsLibrary
Clients can create their own ontology that is interpretable by and relevant to the Neemee ontology, clients can then create
applications that address speciﬁc search functions and data exports for client speciﬁc ontologies.

12.
Protégé LANGUAGE AND SPELLING
MANY DEVELOPERS ARE CREATING PLUGINS FOR Protégé TO TRANSLATE LANGUAGES AND CORRECT SPELLING
In the Entity Uniform Resource Identiﬁer (URI) pane, select
auto ID. When you create a new class, property or
individual P4 will give it a meaningless URI and a readable
label. That way if you exchange ontologies, correcting
spelling mistakes (by merely changing labels) won’t cause
the links between the ontologies to break.
-Protege Jess Mapping Tab (PJMappingTab)
Each terminology of this ontology is designed language
dependent and for each language one separated class is deﬁned
as shown (Fig 1). Word attributes of term sets are gathered
using dictionaries and thesauruses of different languages.
Multilingual equivalents of each word are identiﬁed, regardless
of ambiguity of synonym in different languages. Fig 3 shows a
snapshot of the translation graph in protégé tool using TGviz
plug-in. This approach helps to deﬁne and implement each word
independent of a language and as a bridge between other
languages.
-Protégé Tool and Development of Multilingual Ontology

13.
Protégé COLLABORATION AND SOFTWARE EXPORT
MANY DEVELOPERS ARE CREATING PLUGINS FOR Protégé COLLABORATIVE EDITING AND SOFTWARE EXPORT
You can export the query results to a tab structured ﬁle
(the tabs are conﬁgurable) by clicking on the E icon at the
top of the search result list. The exported ﬁle will contain a
row for each exported entity. The row will contain the
values for the exported slots/properties separate by the
slot delimiter. It can be opened either with a text editor, or
spreadsheet software (e.g., Open Ofﬁce Spreadsheet, or
Microsoft Ofﬁce Excel). The result in Open Ofﬁce will look
as follows (similar also in Excel):
The, Annotations tab shown in Figure 1 allows a user to annotate
the selected tags in the ontology tree. Users may decide to
start a new discussion thread related to the selected tags, or to
reply to an existing comment. We also support different types
of annotations (comment, questions, example, etc.) that can be
selected from the combo­box at the upper right corner of the
collaborative pane.

15.
Protégé VISUALIZATION AND REPORTING
MANY DEVELOPERS ARE CREATING PLUGINS FOR Protégé ONTOLOGY VISUALIZATION AND DATA REPORTING
OVERVIEW OF RELEVANCE DETAIL OF RELEVANCE
Visualizing the ontology as a whole gives clients
the ability to trace the entire structure of
relevance from higher level concepts to the
micro level evidence. The over view answers
question of ‘where did this come from?’ and
‘how do these go together?’ It is the
OVERVIEW OF RELEVANCE form the top to
the bottom
DISTRIBUTION OF RELEVANCE
Visualizing the ontology as a graph or pie chart
gives clients the ability to see the relative
quantities of ontological concepts across the
spectrum of their subject. The DISTRIBUTION
OF RELEVANCE answers question of ‘what is
important?’ and ‘how important is it?’
In a collaborative ontology, the meaning
o f te r m s m u st h a v e a co m m o n
u n d e r sta n d i n g . T h e D E T A I L O F
RELEVANCE provides the framework
sharing deﬁnitions and lineage of terms
within the ontology.

16.
Protégé VISUALIZATION AND REPORTING
MANY DEVELOPERS ARE CREATING PLUGINS FOR Protégé ONTOLOGY VISUALIZATION AND DATA REPORTING
COLLABORATIVE ONTOLOGY COMMUNITY OF CONCEPTS
The Protégé editor can correlate
users and their tags to visualize
clusters of users and their common
interests. The mass collaborative
a s p e ct o f N e e m e e c r e ate s a
COLLABORATIVE ONTOLOGY that
can be used to search users for
relevances to particular concepts
COMMUNITY OF RELEVANCE
In the spirit of Facebook and Switching between views of users and tags
Tw itte r fr ien d visualizat io ns, creates a COMMUNITY OF CONCEPTS form
Neemee can model its community to which groups can arrange relevant search
reveal users that have a great deal ter ms to ﬁn d u sers vice versa. The
in common through their tags but visualization reinforces the personal nature of
may not know how closely related Neemee’s insights.
they are. Building a COMMUNITY
O F R E L E VA N C E b e yo n d t h e
conﬁnes of client groups is at the
core of Neemee’s social insights
methodology.

17.
Protégé NEXT STEPS
1. Generate documentation of existing Neemee code
Document the existing Relational Data Base Management System (RDBMS)
Deﬁne table names and, within each table, the ﬁeld names and any other other
metadata (eg, the ﬁeld type and size).
2. Import Neemee data into Protégé and
produce ontological modeling
Using the DataMaster plug-in, import
schema structure from the RDBMS.
Extend the current database (eg, the
ﬁeld type and size) to support the
kind and type of ontology modeling
to build the reporting and data
visualizations that grow Neemee
a n d C u lt u r e W a v e s p r o d u c t
capabilities.